I have another website where I have posted more information and perspective on coronavirus. here is the content of some of those posts.
Just What the Heck is a Coronavirus?
Some excellent background on coronavirus is contained in a medical textbook found at this link. (coronavirus info) I know this is dated, but I am trying to find something free. And if you are interested in some comparative information on orthomyxoviruses, which is a mouthful and the technical name for influenza viruses, look at that chapter in the same book. One interesting aspect you will notice about coronaviruses is that apparently human antibodies generated once a person has been infected may not have long-lasting protective effects. I am not sure why that should be so, but if true it would be concerning because it would raise the risk of reinfections in future years. There is always Wikipedia. (Wik. Page)
Please note also that coronaviruses are very common, we all likely have been exposed to them and heretofore have largely been an annoyance, rather than the cause of a serious illness, which the exception of the SARS and MERS variations, which were very serious, more so than this strain.
General information can be found at the Centers for Disease Control (CDC Website)
The National Institutes for Health website is at (NIH Website)
If you insist on torturing yourself with coronavirus statistics, here are a couple of sources. Worldometer (Worldometer site)
Johns Hopkins (JH Website)
Statista (Statista site)
And a site that tracks the number of tests conducted and results. Very useful because you can see that even among people who think they have some reason to be tested, the positive rate is under 20%. And you get some interesting commentary on the data issues by state. (test tracking)
How Do I Make Sense of Coronavirus Numbers?
A lot of information is thrown around on supposed infection rates and death rates for the current coronavirus. Here is a suggestion for how to think logically about all these numbers.
1) First, what percent of the population has had exposure to the virus. We don’t know what that is, but I assume a high percent of the population has been or is going to be exposed. But it clearly isn’t 100%. Testing for antibodies is the only way to try to partially determine that number, see below, and some studies are beginning. But antibodies are only generated by people who are infected. Not everyone who is exposed is infected. If there isn’t an antibody specific to this variant, it would be hard to know if the person was exposed or not. I don’t think there is a good way to determine exposure rates.
2) Next, what percent of the population has been infected, which means you were exposed and the virus actually got a toehold in your body. As I note above, not sure how accurately or by what means we can determine how many people have been exposed. Infection rates may be more important in any event, and as best we can tell, that is actually low, only 20% on the Diamond Princess, apparently much lower in Wuhan, where you have to remember the virus ran unchecked for weeks in a densely populated metropolitan area of over ten million people, likely exposing a very large fraction of the population. I have more information on this in a separate post. Why it is low is an interesting question, it suggests some general coronavirus antibodies are present in most people and operating against the virus, or that there is some other protective factor at work for a large percentage of the population. To ascertain infection rates, you would need large scale, randomized testing of the population for antibodies. This number is the one which is most abused by politicians, along with death projections.
3) Then, what percent of the infected will develop an illness, which means you actually develop some symptom due to infection by the virus. Again, as best we can tell from testing rates and other factors, a very large percent of those infected are asymptomatic, at least half and potentially as many as three-quarters of all who are infected. Hence the concern about social distancing, good hygiene, etc. because if you are infected, you can be spreading the virus even if you are asymptomatic. To ascertain the true rate of asymptomatic persons, you would need a large scale randomized testing study for presence of the virus, and you would need to find out how many people who had the virus had a symptom. Since for most people, the illness is pretty mild, you would be relying on self-reporting, which can be unreliable and at this point you would assume many people with any of the typical symptoms are assuming it is due to coronavirus, when it often certainly isn’t. Anyone who has tested positive and seeks medical care would also be included in that percent of symptomatic people.
4) Next, what percent become seriously ill, which you can define as someone who needs hospitalization. This also gets a little tricky, because the people who are most likely to get infected and become seriously ill, generally have multiple serious comorbidities. So is it the virus or the comorbidities that is causing the need for hospitalization. But at least if you got your denominator right, see the note below, the calculation of the percent of people who get seriously ill should be more straightforward than some of the other calculations, because we have good information about diagnoses for hospitalizations. According to Worldometer data, the proportion of infected persons with serious illness has held at around 5% for some time.
5) What percent of the population dies, again not as straightforward as it seems. Italy’s experience is illustrative. Did these very sick patients who also got coronavirus die from coronavirus or the other diseases. In Italy a review of death certificates determined that only a fraction of deaths attributed to coronavirus illness were actually due to it. But if your definition of cause of death is good, percent of deaths is a straightforward calculation.
One final note is the denominator issue. Best in my judgment to use population, which we do know with precision, so all the above numbers are expressed as percents of population or rates per million of population for example. But no matter what is used as your denominator, you have to be clear about it. If you are going to use deaths as a percent of infection, say that is what you are doing and acknowledge that no one knows what the true infection rate is in a large population like the US. Governor Walz and others are using denominators that are not population and not being clear about it. Obviously any denominator other than total population makes the percent or rate look higher and scarier. And any denominator other than population is useless at this point because of the issues mentioned in 1, 2 and 3.
What is My Risk from Coronavirus?
Okay, this is the big one for people, and understandably so. Am I going to get sick and die? Despite media hysteria and politicians throwing wild numbers around, the answer is no for the vast, vast proportion of the population. I have used the Diamond Princess cruise ship unintended experiment repeatedly for people. It is one of our best guides, because it was a contained, large population. A number of analyses of Diamond Princess have been done, all of which do a better job than I can of explaining it. Here are several of those analyses. (Diamond 1) (Diamond 2) (Diamond 3) People offer critiques of these analyses, but it is hard to argue with a scenario where everyone was exposed and everyone was tested.
Now I will do my poor job at what I gathered from these analyses. There were 3711 people on the cruise ship. They all undoubtedly had heavy exposure to coronavirus, for a number of days before any problem was identified. In fact, you would suspect the virus was on the ship before it sailed. All of these people were tested for coronavirus infection. Only a little less than 20% were positive. Out of the positives, a slight majority were asymptomatic, so around 10% of the population had symptoms of illness. Out of the group that experienced any symptoms, a very small number got seriously ill and either 8 or 10 have died depending on the analysis and the variation appears to be due to uncertainty about cause of death. Let’s take the high number, 10. So out of the population on the ship, .27% died. Now consider some obvious relevant factors, the cruise ship population was significantly older than the general population, although probably a healthier old, since they were cruising. These people likely had very extensive virus exposure. So the results from the cruise ship experiment probably overstate the expected real world experience and represent some worst case, upper bound for what we should expect to see. I would expect that when the epidemic has run its course in the United States we will see a population death rate of .1% or less.
And further evidence, depending on your trust in Chinese numbers, comes from the experience of Wuhan, the origination point for the virus. It is a densely populated urban area of over 10 million people. The virus most likely emerged there in late October or early November. It is hard to be precise because the government was attempting to suppress information about a potential new illness. For several weeks, therefore, the virus expanded unchecked both within China and as we know now, around the world. The population in Wuhan likely had high levels of exposure. Yet there too we see infection rates well below 20%, and a very low population death rate, as we do now in other countries. This is also useful evidence. And in the United States, positive rates from fairly widespread testing are below 15%, from a self-selected population that is more likely to be infected.
Early in the virus’ spread, an analysis from Imperial College in the UK put out truly scary numbers on infection and death rates. That analysis basically was a model and models have lots of problems because they can only be as good as the assumptions built into them. As you arbitrarily set parameters, you can literally get just about any answer you want. At the time of publication, there simply wasn’t very good data to make assumptions and set parameters. The publication wasn’t peer-reviewed and it’s conclusions were quickly discredited by epidemiologists and statisticians, but unfortunately the damage was done as the media did its usual job of running the scariest numbers they could find and politicians did their usual job of being stampeded into action without good analysis. The primary author of the study, Neil Ferguson, has now redone his “model” and comes up likely rates and predictions a mere fraction of those in the original study, based on social distancing and other mitigation measures, but as I said, the damage is done. (note that Ferguson is having trouble getting his story straight, he now has apparently backtracked on the backtracking. What is apparent is that his original projections of infection rates is not supported by real life experience, nor is his estimate of death rates and he promoted the worst possible case, he claimed to spur action) (A further note on Ferguson. He was responsible for a wildly inaccurate model of an outbreak of foot and mouth disease in Britain which led to the unnecessary slaughter of millions of animals. So this isn’t his first attempt at sensationalistic modeling. (Ferguson critique)). Dr. Birx, the national coronavirus coordinator has done a very good job in the last couple of days attempting to knock down use of these models, calling them unrealistic and saying their use is unduly frightening the public. (Birx Comments)
Now, if you read the prior post, you saw the guidelines for understanding the numbers that you may see, hear or read. Remember especially the point about denominators when thinking about your risk and what politicians tell you about your risk. At this point, of all the tests that have been done in the United States, and they are mostly being done on people who think they have been exposed or have a symptom, and as I said the rate of positives is around 15% or lower. You can track this rate on a site I linked to in another post. Let’s forget about false positives for a moment, which would lower that number, and just accept the upper bound. Only 15% of people who are tested are infected. But remember the denominator issue. You have a large number of asymptomatic people who aren’t even getting tested because they don’t have symptoms. You probably have some sick people who aren’t sick enough to ask for or just don’t want to get a test. So your denominator is almost certainly much larger and the real percent is likely much lower than 15%. Remember what we said above, the Diamond Princess experiment is almost certainly a worst case infection rate.
This is incredibly good news. It is why the propagators of originally scary estimates of infections and deaths have often reduced their estimate to a mere fraction of the original projection. For some bizarre reason, our Governor Walz, however, decided to use the original discredited estimates. Wonder why. Could it be that he and Governor Cuomo and Governor Newsome and others feel a need to suddenly justify actions they are taking which are destroying jobs and lives? And, not to be cynical, could they be trying to set themselves up to look good when infections and deaths were not as high as they projected, and claim that their actions saved all those lives which were never at risk to begin with.
But now you know enough to analyze what you are hearing for yourself and to share that information with others. And you know that the politicians who try to spread clearly wrong numbers are at best uninformed, but more likely misleading people for whatever reason. And if you are young and healthy you have a nominal risk of any health issue even if infected. If you are older, especially over 60, and if you have any serious illness, especially respiratory or immune system illnesses, your risk is dramatically higher, but still not stratospheric. None of this means we should not be taking aggressive mitigation measures, especially basic hygience, social distancing whenever possible, strict quarantine for those who are or think they could be infected, and protection of vulnerable groups.